A simulated data set of hospitalization Length of Stay (LOS) from 3 sites

datasets

A collection of privacy-preserving distributed algorithms for conducting multi-site data analyses. The regression analyses can be linear regression for continuous outcome, logistic regression for binary outcome, Cox proportional hazard regression for time-to event outcome, Poisson regression for count outcome, or multi-categorical regression for nominal or ordinal outcome. The PDA algorithm runs on a lead site and only requires summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online system (<https://pda-ota.pdamethods.org/>) for safe and convenient collaboration. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.

Jiajie Chen

Privacy-Preserving Distributed Algorithms

Chongliang Luo

Rui Duan

Mackenzie Edmondson

Jiayi Tong

Xiaokang Liu

Kenneth Locke

Yong Chen

Penn Computing Inference Learning (PennCIL) lab 

LOS function

A data frame with 1000 rows and 5 variables:<dl>
 <dt>site</dt>
<dd>site id, 500 'site1', 400 'site2' and 100 'site3'</dd> <dt>age</dt>
<dd>3 categories, 'young', 'middle', and 'old'</dd> <dt>sex</dt>
<dd>2 categories, 'M' for male and 'F' for female</dd> <dt>lab</dt>
<dd>lab test results, continuous value ranging from 0 to 100</dd> <dt>los</dt>
<dd>LOS in days, ranging from 1 tp 28. Treated as continuous outcome in DLM</dd>
</dl>

Format

Length of Stay data — LOS

A data frame with 1000 rows and 5 variables:<dl>
 <dt>site</dt>
<dd>site id, 500 'site1', 400 'site2' and 100 'site3'</dd>

 <dt>age</dt>
<dd>3 categories, 'young', 'middle', and 'old'</dd>

 <dt>sex</dt>
<dd>2 categories, 'M' for male and 'F' for female</dd>

 <dt>lab</dt>
<dd>lab test results, continuous value ranging from 0 to 100</dd>

 <dt>los</dt>
<dd>LOS in days, ranging from 1 tp 28. Treated as continuous outcome in DLM</dd>


</dl>

LOS: Length of Stay data

Description

Usage

Arguments

Format